


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_snake_case	:						Tuple         =							logging.get_logger(__name__)

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Optional[int]	    =    ["input_features"]

							def __init__(				self			:    int       ,      lowerCamelCase			:    str=80       ,      lowerCamelCase			:    List[Any]=16000       ,      lowerCamelCase			:    Dict=160       ,      lowerCamelCase			:    int=30       ,      lowerCamelCase			:    Any=400       ,      lowerCamelCase			:    Optional[Any]=0.0       ,      lowerCamelCase			:    Any=False       ,      **lowerCamelCase			:    Optional[Any]       ,      )      ->       Dict:
														super().__init__(
														    feature_size=lowerCamelCase       ,      sampling_rate=lowerCamelCase       ,      padding_value=lowerCamelCase       ,      return_attention_mask=lowerCamelCase       ,      **lowerCamelCase       ,      )
														__snake_case						:     Union[str, Any]									=  n_fft
														__snake_case						:     List[Any]									=  hop_length
														__snake_case						:     Optional[Any]									=  chunk_length
														__snake_case						:     List[str]									=  chunk_length * sampling_rate
														__snake_case						:     str									=  self.n_samples // hop_length
														__snake_case						:     Any									=  sampling_rate
														__snake_case						:     Dict									=  mel_filter_bank(
														    num_frequency_bins=1 + n_fft // 2       ,      num_mel_filters=lowerCamelCase       ,      min_frequency=0.0       ,      max_frequency=80_00.0       ,      sampling_rate=lowerCamelCase       ,      norm="slaney"       ,      mel_scale="slaney"       ,      )

							def        __snake_case   (				self			:    List[Any]       ,      lowerCamelCase			:    np.array							)      ->       np.ndarray:
														__snake_case						:     Union[str, Any]									=  spectrogram(
														    lowerCamelCase       ,      window_function(self.n_fft       ,      "hann"							)       ,      frame_length=self.n_fft       ,      hop_length=self.hop_length       ,      power=2.0       ,      mel_filters=self.mel_filters       ,      log_mel="log10"       ,      )
														__snake_case						:     Dict									=  log_spec[:, :-1]
														__snake_case						:     Any									=  np.maximum(lowerCamelCase       ,      log_spec.max() - 8.0							)
														__snake_case						:     Union[str, Any]									=  (log_spec + 4.0) / 4.0
														return log_spec

							@staticmethod
							# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
							def        __snake_case   (				lowerCamelCase			:    List[np.ndarray]       ,      lowerCamelCase			:    List[np.ndarray]       ,      lowerCamelCase			:    float = 0.0							)      ->       List[np.ndarray]:
														if attention_mask is not None:
																					__snake_case						:     List[str]									=  np.array(lowerCamelCase       ,      np.intaa							)
																					__snake_case						:     int									=  []

																					for vector, length in zip(lowerCamelCase       ,      attention_mask.sum(-1							)							):
																												__snake_case						:     int									=  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7							)
																												if length < normed_slice.shape[0]:
																																			__snake_case						:     int									=  padding_value

																												normed_input_values.append(lowerCamelCase							)
														else:
																					__snake_case						:     List[str]									=  [(x - x.mean()) / np.sqrt(x.var() + 1E-7							) for x in input_values]

														return normed_input_values

							def __call__(				self			:    Any       ,      lowerCamelCase			:    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,      lowerCamelCase			:    bool = True       ,      lowerCamelCase			:    Optional[int] = None       ,      lowerCamelCase			:    Optional[Union[str, TensorType]] = None       ,      lowerCamelCase			:    Optional[bool] = None       ,      lowerCamelCase			:    Optional[str] = "max_length"       ,      lowerCamelCase			:    Optional[int] = None       ,      lowerCamelCase			:    Optional[int] = None       ,      lowerCamelCase			:    Optional[bool] = None       ,      **lowerCamelCase			:    Optional[Any]       ,      )      ->       BatchFeature:

														if sampling_rate is not None:
																					if sampling_rate != self.sampling_rate:
																												raise ValueError(
																												    F'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
																												    F' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
																												    F' was sampled with {self.sampling_rate} and not {sampling_rate}.'							)
														else:
																					logger.warning(
																					    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
																					    "Failing to do so can result in silent errors that might be hard to debug."							)

														__snake_case						:     Any									=  isinstance(lowerCamelCase       ,      np.ndarray							) and len(raw_speech.shape							) > 1
														if is_batched_numpy and len(raw_speech.shape							) > 2:
																					raise ValueError(F'Only mono-channel audio is supported for input to {self}'							)
														__snake_case						:     Union[str, Any]									=  is_batched_numpy or (
														    isinstance(lowerCamelCase       ,      (list, tuple)							) and (isinstance(raw_speech[0]       ,      (np.ndarray, tuple, list)							))
														)

														if is_batched:
																					__snake_case						:     Any									=  [np.asarray([speech]       ,      dtype=np.floataa							).T for speech in raw_speech]
														elif not is_batched and not isinstance(lowerCamelCase       ,      np.ndarray							):
																					__snake_case						:     Tuple									=  np.asarray(lowerCamelCase       ,      dtype=np.floataa							)
														elif isinstance(lowerCamelCase       ,      np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
																					__snake_case						:     Dict									=  raw_speech.astype(np.floataa							)

														# always return batch
														if not is_batched:
																					__snake_case						:     List[Any]									=  [np.asarray([raw_speech]							).T]

														__snake_case						:     List[str]									=  BatchFeature({"input_features": raw_speech}							)

														# convert into correct format for padding

														__snake_case						:     Union[str, Any]									=  self.pad(
														    lowerCamelCase       ,      padding=lowerCamelCase       ,      max_length=max_length if max_length else self.n_samples       ,      truncation=lowerCamelCase       ,      pad_to_multiple_of=lowerCamelCase       ,      return_attention_mask=return_attention_mask or do_normalize       ,      )

														# zero-mean and unit-variance normalization
														if do_normalize:
																					__snake_case						:     List[str]									=  self.zero_mean_unit_var_norm(
																					    padded_inputs["input_features"]       ,      attention_mask=padded_inputs["attention_mask"]       ,      padding_value=self.padding_value       ,      )
																					__snake_case						:     Optional[int]									=  np.stack(padded_inputs["input_features"]       ,      axis=0							)

														# make sure list is in array format
														__snake_case						:     Optional[int]									=  padded_inputs.get("input_features"							).transpose(2       ,      0       ,      1							)

														__snake_case						:     Tuple									=  [self._np_extract_fbank_features(lowerCamelCase							) for waveform in input_features[0]]

														if isinstance(input_features[0]       ,      lowerCamelCase							):
																					__snake_case						:     Dict									=  [np.asarray(lowerCamelCase       ,      dtype=np.floataa							) for feature in input_features]
														else:
																					__snake_case						:     Any									=  input_features

														if return_attention_mask:
																					# rescale from sample (48000) to feature (3000)
																					__snake_case						:     Tuple									=  padded_inputs["attention_mask"][:, :: self.hop_length]

														if return_tensors is not None:
																					__snake_case						:     Dict									=  padded_inputs.convert_to_tensors(lowerCamelCase							)

														return padded_inputs






							def        __snake_case   (				self			:    str							)      ->       Dict[str, Any]:
														__snake_case						:     Tuple									=  copy.deepcopy(self.__dict__							)
														__snake_case						:     str									=  self.__class__.__name__
														if "mel_filters" in output:
																					del output["mel_filters"]
														return output



from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:Tuple	,  a						:float		)						->					float:
								return 0.0




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     np.ndarray		,    _lowerCamelCase					:     int)   ->       tuple[int | float, int | float]:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   min([-20, np.min(fft_results[1 : samplerate // 2 - 1])])
							__UpperCamelCase      :  Any       =   max([20, np.max(fft_results[1 : samplerate // 2 - 1])])
							return lowest, highest




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     FilterType		,    _lowerCamelCase					:     int)   ->       None:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   512
							__UpperCamelCase      :  List[Any]       =   [1] + [0] * (size - 1)
							__UpperCamelCase      :  List[Any]       =   [filter_type.process(_lowerCamelCase) for item in inputs]

							__UpperCamelCase      :  Optional[Any]       =   [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__UpperCamelCase      :  Optional[int]       =   np.abs(np.fft.fft(_lowerCamelCase))
							__UpperCamelCase      :  Optional[int]       =   20 * np.logaa(_lowerCamelCase)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24		,    samplerate / 2 - 1)
							plt.xlabel("Frequency (Hz)")
							plt.xscale("log")

							# Display within reasonable bounds
							__UpperCamelCase      :  Optional[Any]       =   get_bounds(_lowerCamelCase		,    _lowerCamelCase)
							plt.ylim(max([-80, bounds[0]])		,    min([80, bounds[1]]))
							plt.ylabel("Gain (dB)")

							plt.plot(_lowerCamelCase)
							plt.show()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     FilterType		,    _lowerCamelCase					:     int)   ->       None:






							'''simple docstring'''





							__UpperCamelCase      :  Any       =   512
							__UpperCamelCase      :  Dict       =   [1] + [0] * (size - 1)
							__UpperCamelCase      :  Tuple       =   [filter_type.process(_lowerCamelCase) for item in inputs]

							__UpperCamelCase      :  Dict       =   [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__UpperCamelCase      :  Optional[int]       =   np.angle(np.fft.fft(_lowerCamelCase))

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24		,    samplerate / 2 - 1)
							plt.xlabel("Frequency (Hz)")
							plt.xscale("log")

							plt.ylim(-2 * pi		,    2 * pi)
							plt.ylabel("Phase shift (Radians)")
							plt.plot(np.unwrap(_lowerCamelCase		,    -2 * pi))
							plt.show()